Noise-robust real-time extraction of the respiratory motion signal from pet list-data

ABSTRACT

A respiratory motion signal generation method operates on emission data ( 22 ) of an imaging subject in an imaging field of view (FOV) acquired by a positron emission tomography (PET) or single photon emission computed tomography (SPECT) imaging device ( 10 ). An array of regions ( 32 ) is defined in the imaging FOV without reference to anatomy of the imaging subject. For each region of the array of regions defined in the imaging FOV, an activity position versus time curve ( 54 ) is computed from the emission data acquired by the PET or SPECT imaging device. Frequency-selective filtering of the activity position versus time curves is performed to generate filtered activity position versus time curves. At least one motion signal ( 66 ) is generated by combining the filtered activity position versus time curves of at least a selected sub-set of the regions.

FIELD

The following relates generally to the medical imaging arts, emissionimaging arts, positron emission tomography (PET) imaging arts, singlephoton emission computed tomography (SPECT) imaging arts, patientmonitoring arts, respiratory monitoring arts, and related arts.

BACKGROUND

In emission imaging, such as positron emission tomography (PET) orsingle photon emission computed tomography (SPECT), a patient or otherimaging subject is administered a radiopharmaceutical designed topreferentially accumulate in a target organ or tissue and that includesa radioactive isotope, e.g. a positron-emitting isotope in PET. Theimaging subject is loaded into the imaging device (e.g. a PET scannerfor PET imaging, or a gamma camera for SPECT imaging) and emissionimaging data are collected and reconstructed, typically using aniterative reconstruction algorithm, to generate a reconstructed image.For improved accuracy, an attenuation map of the imaging subject may beprovided, for example computed from a transmission computed tomography(CT) image of the subject, and the attenuation map is used to correctthe reconstruction for attenuation of the detected radiation (e.g. 511keV gamma rays in the case of PET) in the body of the imaging subject.

Depending upon the size of the region of interest, the subject mayremain in a single fixed position for the entire emission imagingsession; or, if a larger volume is to be imaged than can be captured ina single field-of-view (FOV) of the imaging device then multi-stageimaging may be employed in which the subject support (e.g. couch) movesthe patient stepwise through the imaging FOV with a separate imageacquired at each step. Continuous patient motion is also a possibility,i.e. the patient may be moved through the FOV in continuous fashionduring the imaging data acquisition and the resulting data adjusted forpatient position at time of acquisition to generate an image larger thanthe imaging device FOV.

A known source of image degradation is motion of the subject. One suchmotion source is respiration. One way to reduce respiration motionartifacts is to process data acquired during a single respiratoryphase—for this purpose, end-expiration is often chosen as it isrelatively quiescent and of relatively long duration (typically ˜30% ofthe respiratory cycle). The respiration may be monitored using abreathing belt or other dedicated device. However, such a device can beuncomfortable for the patient, and may contribute to scattering and/orabsorption of the measured radiation, thereby degrading image quality.

Other approaches, known as “data driven” approaches, attempt to extractthe respiration signal from the emission imaging data. Such approachesare effective in the case of an imaging modality such as transmissioncomputed tomography (CT) imaging, in which a strong signal is obtainedfrom which an anatomical feature such as the lung/diaphragm interfacemay be delineated and monitored for respiratory motion. Data drivenapproaches are less readily applied to emission imaging. This is due tothe low radiopharmaceutical dosage employed in the interest of patientradiological safety, which results in low emission signal strength andconsequent low signal-to-noise ratio (SNR). Moreover, emission imagingtypically captures functional information, e.g. high metabolismcarcinogenic tumors are typically the “bright” features or “hot spots”due to high vasculature in the tumor leading to high concentration ofthe administered radiopharmaceutical in the tumor.

Kesner, U.S. Pub. No. 2008/0273785 (“Kesner”) discloses a data-drivenapproach for extracting a respiratory signal for retrospective gating ofPET images. In this approach, a time series of PET images is generatedat 0.5 sec time intervals, and a frequency-filtered activity-versus-timecurve is extracted for each voxel of the image. By filtering to limit torespiratory frequency, the activity-versus-time curves are expected tocorrelate with respiration, and these are combined to generate therespiratory signal. Voxel weighting may be employed, with voxel weightsbeing based on the mean value of the activity-versus-time curve, orbased on proximity of the voxel to spatial gradients. Some voxel weightsmay be set to zero so as to exclude those voxels from contributing tothe combined respiratory signal.

The following discloses new and improved apparatuses and methods.

SUMMARY

In one disclosed aspect, an emission imaging data processing devicecomprises an electronic processor and a non-transitory storage mediumstoring instructions readable and executable by the electronic processorto perform a respiratory motion signal generation method as follows. Apositron emission tomography (PET) or single photon emission computedtomography (SPECT) imaging device is operated to acquire emission dataof an imaging subject in an imaging field of view (FOV). For each regionof an array of regions defined in the imaging FOV, an activity positionversus time curve is computed from the emission data acquired by the PETor SPECT imaging device. At least one respiratory motion signal isgenerated by combining the activity position versus time curves of atleast a sub-set of the regions of the array of regions afterfrequency-selective filtering of the activity position versus timecurves to select content in a respiratory frequency band.

In another disclosed aspect, a motion signal generation method operateson emission data of an imaging subject in an imaging FOV acquired by aPET or SPECT imaging device. The motion signal generation methodcomprises: defining an array of regions in the imaging FOV withoutreference to anatomy of the imaging subject; for each region of thearray of regions defined in the imaging FOV, computing an activityposition versus time curve from the emission data acquired by the PET orSPECT imaging device; performing frequency-selective filtering of theactivity position versus time curves to generate filtered activityposition versus time curves; and generating at least one motion signalby combining the filtered activity position versus time curves.

In another disclosed aspect, an emission imaging data processing deviceis disclosed, including a PET or SPECT imaging device, an electronicprocessor, and a non-transitory storage medium storing instructionsreadable and executable by the electronic processor to perform arespiratory motion signal generation method. That method includes:operating the PET or SPECT imaging device to acquire emission data of animaging subject in an imaging FOV; computing activity maps from theemission data for successive time intervals in a region defined in theimaging FOV; computing a transaxial activity position versus time curvefrom the activity maps, the transaxial activity position comprising aminimum distance of the centroid of the activity map from an axialanatomical axis (z) of the imaging subject; and generating a respiratorymotion signal based on at least the transaxial activity position versustime curve.

One advantage resides in generation of a respiratory signal with reducednoise.

Another advantage resides in providing more robust respiratory gating ofemission imaging.

Another advantage resides in providing a respiratory signal generatedwith low time latency so as to be displayed concurrently with emissionimaging data collection.

Another advantage resides in providing automated respiratory signalgeneration from emission data without reference to anatomy of theimaging subject.

Another advantage resides in providing automated respiratory signalgeneration from emission data which detects whether respiratory motionis present.

Another advantage resides in providing more than one respiratory signalgeneration from emission data, which enables detection of phase-shiftbetween respiratory motion in e.g. upper abdomen and lower abdomen.

A given embodiment may provide none, one, two, more, or all of theforegoing advantages, and/or may provide other advantages as will becomeapparent to one of ordinary skill in the art upon reading andunderstanding the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may take form in various components and arrangements ofcomponents, and in various steps and arrangements of steps. The drawingsare only for purposes of illustrating the preferred embodiments and arenot to be construed as limiting the invention.

FIG. 1 diagrammatically illustrates an emission imaging device providingimaging and also data driven generation of a respiration motion signal.

FIG. 2 diagrammatically illustrates data driven generation of arespiration motion signal as suitably performed by the device of FIG. 1.

FIGS. 3-6 diagrammatically show aspects of the data driven method ofgeneration of the respiration motion signal shown in FIG. 2.

DETAILED DESCRIPTION

Approaches for retrospective respiratory gating, such as that of Kesnerdiscussed previously herein, have certain disadvantages. They entailperforming an image reconstruction for each time interval (e.g. 0.5 sectime windows in Kesner) in order to determine the activity at each voxelin each time interval. A trade-off is made between temporal resolution(improved by using a shorter time window) and noise (improved for eachreconstructed image by using a longer time window). The imagereconstruction is computationally costly, is not conducive to real-timerespiratory signal extraction, and cannot leverage time-of-flightlocalization in an efficient manner.

The noise can in principle be reduced by combining theactivity-versus-time curves of all voxels in the imaging field of view(FOV) to generate the respiratory signal. However, most voxels will nothave a strong respiratory signal component, and thus may contribute morenoise than signal to the combined respiratory signal. Voxel weightingbased on the mean value of the activity-versus-time curve, or based onproximity of the voxel to spatial gradients, may be used topreferentially combine voxels with a stronger respiratory cyclingcomponent. However, weighting based on the mean value of theactivity-versus-time curve can itself be noisy due to noise of thatcurve; while, weighting based on proximity to spatial gradients requiresprocessing of the reconstructed images to identify regions of largespatial gradients, and even so those gradients may not be associatedwith a strong respiratory signal component.

Data driven respiratory motion signal generation techniques disclosedherein overcome these and other disadvantages of existing techniques.The approaches disclosed herein are anatomy-agnostic and do not requirea priori knowledge of (or post-reconstruction processing identificationof) regions of large spatial gradient or other anatomical features ofthe patient. The disclosed approaches are well-suited to emissionimaging as they automatically focus the respiratory signal extraction onso-called “hot spots”, i.e. lesion-sized regions of high radioactivitythat move with respiratory motion. The disclosed approaches areregion-based, rather than voxel-based, and are expected to exhibitimproved SNR. The regions may be tailored to the expected lesion size,but are not required to match with extant lesions either in terms oflocation or size. Still further, the disclosed approaches automaticallydetect when the imaging FOV is not strongly affected by respiratorymotion, in which case respiratory motion correction may be appropriatelyomitted.

The disclosed respiratory motion signal generation approaches havefurther advantages. They do not employ computationally costly imagereconstruction, but instead employ back-positioning. In general,backpositioning is performed by calculating, for a detected decay event,a most likely decay event location in three-dimensional space. In thecase of time-of-flight (TOF) positron emission tomography (PET) imaging,the back-positioning may advantageously leverage TOF localization alonga line-of-response (LOR) to provide more accurate back-positioning. Inthis case, each event is suitably assigned a location probability alongthe LOR according to a Gaussian distribution model (or other chosenprobability distribution), and the backpositioning for that event iswhere the maximum probability value is found. In the case of non-TOF PETor single photon emission computed tomography (SPECT) imaging, theback-positioning can be performed by, for example, setting theback-position value of each voxel to the count of LORs or SPECTprojections that pass through the voxel. In either case, rapidgeneration of an activity map by back-positioning facilitates performingreal-time extraction of a respiratory motion signal if desired. Stillfurther, the disclosed approaches generate an activity position curvefor each region thereby providing additional information as comparedwith a simple voxel activity versus time curve. In some embodiments ofthe disclosed approaches, both transaxial and transverse motion curvesare generated, which can more effectively capture the spatialcharacteristics of the respiratory motion.

With reference to FIG. 1, an illustrative emission imaging systemcomprises a combined positron emission tomography (PET)/transmissioncomputed tomography (CT) imaging device 8, which includes both a PETimaging gantry or scanner 10 and a CT gantry or scanner 12 mounted withcoaxial bores such that a patient may be loaded onto a common patienttable 14 and loaded into either the CT gantry 12 for CT imaging or thePET gantry 10 for PET imaging. The PET imaging gantry or scanner 10 hasradiation detectors for detecting 511 keV gamma rays, and a line ofresponse (LOR) is defined by two substantially simultaneous gamma raydetections presumed to originate from a single positron-electronannihilation event. In some embodiments, the radiation detectors of thePET gantry are high-speed detectors which are capable of detecting thetime difference between the detections of the two 511 keV gamma raysemitted by a single positron-electron annihilation event. This measuredtime difference enables further time-of-flight (TOF) localization of thepositron-electron annihilation event along the LOR. Each LOR is timestamped with the acquisition time (the finite TOF difference is usuallyon the order of picoseconds, and for LOR time stamping purposes isnegligible). The CT gantry 12, if provided, acquires transmission CTimages 16 which may, for example, be used to generate an attenuation map18 by appropriate conversion of the Hounsfield numbers of the CT image16 to corresponding absorption values at 511 keV (the energy of gammarays emitted during positron-electron annihilation events). By way ofnon-limiting illustrative example, the illustrative PET/CT imagingdevice imaging scanner 8 may be the PET gantry of a Vereos™ DigitalPET/CT scanner, available from Koninklijke Philips N.V., Eindhoven, theNetherlands.

The illustrative emission imaging device is a PET imaging device 10which acquires emission imaging data in the form of time stamped LORs;in other embodiments the emission imaging device may be a gamma camerawhich acquires emission imaging data in the form of single photonemission computed tomography (SPECT) projection data. In SPECT imaging,each projection is defined by a single radiation photon or particledetection event, and is again time stamped. As is known in the art, theprojections in SPECT imaging are spatially limited to a plane or (morecommonly) to a narrow-angle cone or line, through the use of acollimator made of radiation-absorbing high atomic weight (high-Z)material, such as lead or a lead compound, which is mounted on theradiation detector head. As with PET imaging, the optional CT gantry 12can generate a CT image 16 that is converted to an attenuation map 18for performing attenuation correction during the SPECT reconstruction.

As diagrammatically shown in FIG. 1, an electronic processor 20processes emission data 22 acquired by the PET imaging gantry or scanner10 (comprising LORs in the illustrative PET imaging embodiment, orcomprising projections acquired by a gamma camera in an alternativeSPECT imaging embodiment) to generate a respiratory motion signal and togenerate a reconstructed image. The electronic processor 20 may, forexample, be embodied as a computer 24 (e.g. a desktop computer,network-based server computer, a dedicated imaging device controlcomputer, various combinations thereof, or so forth) that executesinstructions read from one or more non-transitory storage media (e.g.one or more hard drives, optical disks, solid state drives or otherelectronic digital storage devices, various combinations thereof, or soforth) that stores the instructions. The computer 24 typically includesor has operative access to at least one display 26 (e.g. an LCD display,plasma display, or so forth) for displaying reconstructed images, andoptionally also including one or more user input devices such as anillustrative keyboard 28, an illustrative trackpad 29 (or mouse,trackball, touch-sensitive overlay of the display 26, or other pointingdevice), or so forth.

The emission imaging data 22 is acquired over a relatively extendedperiod, that is, over a time interval encompassing many breathsperformed by the imaging subject, in order to provide enough emissionimaging data to achieve an acceptable signal to noise ratio (SNR). Asdiagrammatically indicated in FIG. 1, the electronic processor 20 isprogrammed by instructions stored on a non-transitory storage medium toperform a respiratory motion signal generation process 30 which operateson the emission imaging data 22. The illustrative respiratory motionsignal generation process 30 operates on a pre-defined set of regiondefinitions 32. For example, the image field of view (FOV) of the PETimaging gantry or scanner 10 may be divided into regions comprising aspatial array of spherical (or cylindrical, or otherwise-shaped) regionswhich preferably overlap so as to cover substantially the entire imagingFOV. Time intervals are also defined, typically as time bins of aselected duration, e.g. one second per time interval in one illustrativeexample. The time intervals may optionally overlap temporally. For eachregion of the array of regions 32 and each time interval, back-positionand motion descriptor processing 36 are performed to generate a positiondescriptor (or a plurality of position descriptors, in alternativeembodiments). For example, a position descriptor may comprise thecentroid of the activity in the region along the z-direction (transaxialposition). Additionally or alternatively, a position descriptor maycomprise the radial distance of the centroid of the activity in theregion from a center of the region in the x-y plane (transverseposition; that is, the descriptor is the minimum distance of thecentroid of the activity map from the axial anatomical axis, z, of theimaging subject). Region selection and motion descriptor combinationprocessing 38 then combines the position descriptors of those regionshaving strongest indication of respiratory motion to generate the finalrespiratory motion signal.

The electronic processor 20 is further programmed by instructions storedon (the same or a different) non-transitory storage medium to perform arespiration-gated image reconstruction process 40 that operates on theemission data 22, the respiratory motion as estimated by the respiratorymotion signal generation process 30, and optionally further based on theattenuation map 18, to perform attenuation correction of thereconstructed PET image. The illustrative respiration-gated imagereconstruction process 40 operates to reconstruct a sub-set of theemission data 22 corresponding to a selected respiratory phase(typically end-exhalation, as this phase is quiescent and of longduration) to generate a reconstructed image with reduced blurring due torespiratory motion. For example, the image reconstruction 40 may employan iterative image reconstruction technique such as maximumlikelihood-expectation maximization (MLEM), ordered-subsetexpectation-maximization (OSEM), or so forth, and may optionally includeregularization using an edge-preserving noise-suppressing prior, scattercorrection, or other known techniques for enhancing image quality.

The illustrative electronic processor 20 is further programmed byinstructions stored on (the same or a different) non-transitory storagemedium to perform image display processing 42 to generate avisualization of the reconstructed image, such as a singletwo-dimensional (2D) slice image, a 2D maximum intensity projection(MIP), a three-dimensional (3D) rendering of the volumetricreconstructed image, or so forth, which may be displayed on the at leaston display 26, and/or stored to a Picture Archiving and CommunicationSystem (PACS), and/or printed by a printing device, and/or otherwiseutilized.

With continuing reference to FIG. 1 and with further reference to FIG.2, an illustrative embodiment of the respiratory motion signalgeneration process 30 is described. In this illustrative example, theemission data 22 comprise a time of flight (TOF) positron emissiontomography (PET) list mode data stream. Each emission datum comprises aline of response (LOR) defined between two nearly simultaneous (i.e.within a defined coincidence time window) 511 keV gamma ray detections,with TOF localization along the LOR based on the time difference (orlack thereof) between the two 511 keV gamma ray detection events. In anoperation 50, the emission data acquired over each time interval areback-positioned to generate an activity map for each time interval andfor each region of the array of regions 32. The operation 50 can becarried out by performing the backpositioning for all emission data inthe time window to generate an activity map for the entire imaging FOVand then spatially segmenting that activity map to generate activitymaps for the individual regions of the array of regions 32.Alternatively, the operation 50 can be carried out by performingbackpositioning separately for each region of the array of regions 32.In the illustrative example of TOF PET emission data, the activity mapof the region is generated by back-positioning each LOR to its maximumTOF likelihood position along the LOR. (This differs from imagereconstruction which makes use of the TOF probability distribution alongthe LOR to provide more accurate image reconstruction compared with theactivity map generated in operation 50 by backpositioning).

In an operation 52, for each region of the array of regions 32 and foreach time interval, a value of a position descriptor is computed. Forexample, a position descriptor may comprise the centroid of the activityin the region along the z-direction (transaxial position). Additionallyor alternatively, a position descriptor may comprise the radial distanceof the centroid of the activity in the region from a center of theregion in the x-y plane (transverse position). The result of theoperation 52 is an activity position versus time curve 54 for eachregion of the array of regions 32.

In an operation 60, a sub-set of regions are selected from the array ofregions 32 based on whether the region's activity position versus timecurve satisfies a region selection criterion 62, such as the fraction ofenergy in a respiratory frequency band (e.g., between 0.05 Hz and 0.50Hz in one illustrative example, corresponding to a range of 2-20seconds/breath) being greater than some threshold. It is contemplated toemploy an adjusted respiratory frequency band for special cases, e.g.infant imaging subjects. Typically, it is expected that the operation 60will select a relatively small number of the regions of the array ofregions 32, e.g. perhaps 10-20 regions or fewer may be selected. Theseselected regions are expected to be regions that contain at least aportion of a hot spot over at least a portion of the breathing cycle,with the hot spot being positioned in a lung, thoracic diaphragm, orother anatomical feature that moves strongly with respiration. The hotspot may, by way of non-limiting illustrative example, be a tumorlesion, or a myocardium muscle, liver edge or other anatomical featurethat exhibits a high level of activity. Advantageously, although the hotspots may correlate with anatomy, the operation 60 does not rely uponany a priori knowledge of the anatomy—rather, the optimal sub-set ofregions is selected in the operation 60 in empirical fashion, based onthe criterion 62.

In an operation 64 the activity position versus time curves of theregions selected in the operation 60 are combined to generate arespiratory motion signal 66. In one approach, the operation 64 includesperforming frequency-selective filtering of the activity position versustime curves to select content in the respiratory frequency band (e.g.using a bandpass filter with a pass band of 0.05-0.50 Hz in someembodiments). A correlation is computed of each filtered activityposition versus time curve with a reference respiratory motion signal.The reference respiratory motion curve may be the filtered activityposition versus time curve of the region that best satisfies the regionselection criterion 62, e.g. the curve having the greatest fraction ofits energy in the respiratory frequency band in accord with oneillustrative criterion. The filtered activity position versus time curveis then added to, or subtracted from, the respiratory motion signal,where the adding or subtracting operation is chosen based on the sign ofthe correlation.

With reference to FIGS. 3-6, the use of the array of regions 32 isfurther described by way of illustration. FIG. 3 depicts an imagingsubject 70 imaged in an imaging FOV 72, which is a volume. The array ofregions 32 is defined in the imaging FOV 72. The illustrative regions ofthe illustrative array of regions 32 are spherical regions which overlapso as to substantially fill the imaging FOV 72, as shown in FIG. 3. Alsoindicated in FIG. 3 are two hot spots 74, 76, which may for example bemalignant tumors of high metabolic activity that preferentially absorbthe radiopharmaceutical and hence appear as regions of high activity inthe PET imaging. FIG. 4 illustrates an enlarged view in the vicinity ofthe two hot spots 74, 76, with one particular region 32 o labeled whichcontains the hot spot 76. FIG. 5 illustrates movement of the hot spot 76with respiration, with successive time intervals labeled “a”, “b”, “c”,. . . , “n”, “o”, and “p”. FIG. 6 shows a plot of the centroid of theactivity for each time interval “a”, “b”, “c”, . . . , “n”, “o”, and“p”, and the resulting activity position versus time curve 66 plotted.The drawings are diagrammatic representations: in some expected actualimplementations, the typical highest motion amplitudes are anticipatedto be on the order of 2-3 cm or less—so in practice the trajectory ofmotion affected features is usually contained by at least a singlespherical region. In FIG. 5, given a typical region's diameter of 10 cm,a large motion trajectory including e.g. all positions a,b, . . . , h asillustrated in FIG. 6 may therefore be an unusual case, and a moretypical motion trajectory may include only a,b,c,d, or only e,f,g,h.

The disclosed approaches for generating a respiratory signal are bothfast and noise-robust, and provide enhanced accuracy both in the derivedamplitude and respiratory cycle-gating signal extraction. In thedisclosed approaches, an array 32 of many overlapping regions (e.g.400-500 spherical regions) are considered and their activity position(e.g. centroid) tracked. By this, simultaneous motion components inopposite direction do not cancel out each other by global averaging, andmotion information of small tumors is less masked by noise contained inthe emission data outside the particular region. For all localrespiratory motion curves (one for each of the aforementioned regions)an individual likelihood is computed whether the region containssignificant respiratory motion information (e.g. via operation 60 ofFIG. 2). In a suitable approach, this employs a selection criterion 62that is based on the fraction of respiration-characteristic frequenciesin each signal curve. Only high likelihood signal components (i.e.regions) are used for calculating a global motion signal 66. By this,most of the detected noise is excluded from the global respiratorysignal estimate. In order to decide, whether the activity positionversus time curve of a region should be added or subtracted in thesignal combination process 64, the cross-correlation between the(intermediate) global curve estimate and each new region curve issuitably calculated. Using the resulting sign information, motions inopposite spatial directions are not cancelled out. Instead, the qualityof the final respiratory signal curve 66 is improved.

In addition to the average axial (z-direction) position of the list-modeevents, also transaxial motion information is optionally included and bythis the motion signal-to-noise ratio is further increased in theprocessed data. This is achieved via a patient motion modeling using twomain components of a cylindrical coordinate system, the radial component(in the transaxial plane) and z-component (in axial direction).

Advantageously, the disclosed respiratory motion signal generationmethod can be performed in real-time after an initial acquisition phasewhere the most motion-affected regions have been identified. In FIG. 2,this corresponds to (after the initial selection of regions viaoperation 60), repeating only the activity position versus time curveupdating for those selected regions (corresponding to repeatingoperations 50, 52 performed only for the selected regions after theinitial time intervals) and performing the combining operation 64 onlyfor the selected regions. Said another way, the selection 60 of thesub-set of the regions of the array of regions 32 is performed forinitial successive time intervals. After the selection of the sub-set ofregions of the array of regions, the computing of the activity positionversus time curve 54 and the generating of the respiratory motion signal66 is performed using only the selected sub-set of regions of the arrayof regions for subsequent successive time intervals that follow theinitial successive time intervals. Such processing can be done inreal-time, given sufficient processing power. Optionally, the regionselection operation 60 may be repeated occasionally, e.g. each 30seconds updates of the region likelihoods.

Accuracy of the activity position versus time curve 54 for each regiondepends in part on how accurately the activity maps generated by thebackpositioning operation 50 reflect the true activity distribution. Aspreviously mentioned, the backpositioning operation 50 for TOF PET mayutilize the TOF localization information along the LOR. Advantageously,this TOF localization has been trending toward higher spatial resolution(i.e. tighter spatial localization) as successive generations of TOF PETdetectors with improved temporal resolution are developed. Accordingly,the disclosed approach should exhibit increasing accuracy with continuedimprovements in TOF localization resolution.

In the following, some further examples of illustrative embodiments aredescribed. The array of regions 32 suitably includes a number ofoverlapping, e.g., spherical regions in the imaging FOV, as shown inFIG. 3. For each time interval t with time width T, one or more averageactivity position descriptors of corresponding TOF events are calculatedfor each region (optionally during the PET emission data acquisition, oralternatively retrospectively after the acquisition is completed). Asillustrative examples, two different activity position descriptors d areused in PET, based on cylindrical coordinates: (i) the local z-axisposition representing craniocaudal motion, and (ii) the radial distancefrom z-axis, containing anteroposterior and mediolateral motioncomponents. A frequency analysis is performed for the resulting activityposition versus time curves s_(r,d) (t) of each region r and eachdescriptor d. The analysis provides the signal energy fraction within apre-defined respiratory frequency band, symbolically represented hereusing operator BP (e.g., between ½ Hz and 1/18 Hz, i.e. between0.05-0.50 Hz), to extract the fraction of energy in the respiratoryfrequency band:

$\begin{matrix}{S_{r,d} = \frac{\sum_{\forall\; t}\left( {{BP}\left( {s_{r,d}(t)} \right)} \right)^{2}}{\sum_{\forall\; t}\left( {s_{r,d}(t)} \right)^{2}}} & (1)\end{matrix}$

where BP( . . . ) is an operator which extracts the component in therespiratory frequency band (e.g. 0.05-0.50 Hz in some embodiments).S_(r,d) is referred to as the fraction of energy in the respiratoryfrequency band, and can be considered as likelihood that a respiratorymotion affected feature is at least partially located inside region r.After normalization using:

$\begin{matrix}{\overset{\_}{s(t)} = \frac{s(t)}{\sqrt{E\left( {s(t)} \right)}}} & (2)\end{matrix}$

where E(s(t)) is the signal energy given by:

$\begin{matrix}{{E\left( {s(t)} \right)} = \frac{\sum_{\forall\; t}\left( {s(t)} \right)^{2}}{L}} & (3)\end{matrix}$

where L is the discrete signal length with a sampling rate of r=1/T, allresulting respiratory signal curves

are sorted using corresponding S_(r,d), and the highest fraction signalcurves are combined in order to reduce noise artifacts. In thisillustrative example, the region selection criterion 62 which is usedis:

$\begin{matrix}{S_{r,d} \geq {\alpha_{thres} \cdot {\max\limits_{r,d}\left\{ S_{r,d} \right\}}}} & (4)\end{matrix}$

with, e.g., α_(thres)=0.9 in some illustrative embodiments.

For combining the selected signal curves (FIG. 2 operation 64), theglobal signal curve is first initialized with the highest fractioncurve. Subsequently, all remaining selected signal curves are added orsubtracted depending on correlation coefficient c, with:

$\begin{matrix}{{c\left( {{s_{1}(t)},{s_{2}(t)}} \right)} = {\sum\limits_{\forall\; t}^{\;}{{s_{1}(t)} \cdot {s_{2}(t)}}}} & (5)\end{matrix}$

according to:

s _(global) →s _(global)+sign(c(s _(global) ,s _(r,d)))·s _(r,d)  (6)

for all r and d satisfying Equation (4).

Finally, the resulting global signal curve s_(global) is normalizedaccording to Equation (2) in order to make it easy comparable to, e.g.,a measured reference signal. Note, that s_(global) does not containinformation about the spatial amplitude or the motion's trajectory, butit can be used for gating of the PET list-mode data and subsequentmotion compensated PET imaging.

For real-time applications in which the respiratory motion signal isgenerated during acquisition of the emission data, if insufficientcomputational power is available to perform signal processing and signalcombining algorithms for each individual time frame (e.g. 0.5 s), thenS_(r,d) can be computed and correlation signs are used in Equation (6)for each region after an initial acquisition period (e.g. 30 s). Then,once the fractions and signs of correlations are initially known, thecombination of single signals can be reduced to a simple summationaccording to Equation (6). The fractions and correlation signs can beupdated from time to time in order to iteratively increase signalestimation accuracy.

With returning reference to FIG. 2, the operation 60 selects thoseregions of the array of regions 32 exhibiting strong respiratory motion.However, in some situations there may be no strong respiratory motion.For example, consider a “whole body” scan in which the patient is movedstepwise to scan from head to toe. Scans with the patient positionedwith the torso in the imaging FOV can be expected to exhibit strongrespiratory motion. By contrast, scans with the patient' head in theimaging FOV, or with the patient's lower legs in the imaging FOV, arelikely to exhibit negligible respiratory motion. In such cases, thenumber of regions selected in the operation 60 may be small, or evenzero. This enables automated detection of situations in which norespiratory motion gating is needed. For example, the respiratory motionsignal generator may optionally output a zero value for the respiratorymotion signal 66 if the selected sub-set of regions of the array ofregions includes less than a threshold number of regions satisfying theregion selection criterion 62. Referring back to FIG. 1, therespiration-gated image reconstruction 40 may then omit respiratorygating and instead reconstruct all the emission data in cases where therespiratory motion signal is identically equal to zero.

In the illustrative embodiments, the region selection/motion descriptorcombination processing 38 (e.g., illustrative operations 60, 64 of FIG.2) operate to produce a singular respiratory motion signal 66. However,in other contemplated embodiments this processing may produce two (ormore) respiratory motion signals for different portions of the imagingFOV. For example, a phase-shift in the physical respiratory motionsignal may be present between respiratory motion in the upper abdomenversus the lower abdomen. Various approaches can be employed. In oneapproach, an a priori known anatomical compartmentalization of theimaging FOV may be employed, and the disclosed processing (e.g. that ofFIG. 2) applied separately to each different compartment (i.e. portion)of the imaging FOV. In another contemplated approach, the activityposition versus time curves 54 may be empirically analyzed to identifytwo or more contiguous regions of the imaging FOV whose regions exhibitdifferent phase offsets of their activity position versus time curves.One way to implement this, employing the framework of Equations (1)-(6),is to use the correlation c(s_(global),s_(r,d)) to assign a phase toeach activity position versus time curve, which can be used togetherwith the spatial layout of the regions of the array of regions 32 toidentify contiguous portions of the imaging FOV having approximately thesame phase. In this approach, the output may include two differentrespiratory motion signals, e.g. one for the upper abdomen and anotherfor the lower abdomen.

The illustrative embodiments generate a respiratory motion signal.However, analogous processing may additionally or alternatively beperformed to generate a cardiac motion signal. To do so, the timeinterval is reduced to a smaller value appropriate for the fastercardiac cycling, e.g. 0.5/(5 Hz)=0.1 sec in one non-limiting example,and the respiratory frequency band is suitably replaced by a cardiacfrequency band encompassing a credible range of cardiac heart rates,e.g. 0.6 Hz to 5.0 Hz in one illustrative example.

The invention has been described with reference to the preferredembodiments. Modifications and alterations may occur to others uponreading and understanding the preceding detailed description. It isintended that the invention be construed as including all suchmodifications and alterations insofar as they come within the scope ofthe appended claims or the equivalents thereof.

1. An emission imaging data processing device comprising: an electronicprocessor; and a non-transitory storage medium storing instructionsreadable and executable by the electronic processor to perform arespiratory motion signal generation method including: operating apositron emission tomography (PET) or single photon emission computedtomography (SPECT) imaging device to acquire emission data of an imagingsubject in an imaging field of view (FOV); for each region of an arrayof regions defined in the imaging FOV, computing an activity positionversus time curve from the emission data acquired by the PET or SPECTimaging device; and generating at least one respiratory motion signal bycombining the activity position versus time curves of at least a sub-setof the regions of the array of regions after frequency-selectivefiltering of the activity position versus time curves to select contentin a respiratory frequency band; wherein the computing of the activityposition versus time curve from the emission data acquired by the PET orSPECT imaging device includes: generating an activity map of the regionfor successive time intervals from the emission data acquired by the PETor SPECT imaging device; and computing values of a statistical activityposition descriptor for the activity maps of the region for thesuccessive time intervals; and wherein the statistical activity positiondescriptor includes a position of the centroid of the activity map alonga transverse direction parallel with an axial anatomical axis (z) of theimaging subject, and/or a minimum distance of the centroid of theactivity map from an axial anatomical axis (z) of the imaging subject.2. The emission imaging data processing device of claim 1 wherein therespiratory motion signal generation method further includes: selectinga sub-set of the regions of the array of regions for which the activityposition versus time curve includes content in the respiratory frequencyband satisfying a region selection criterion; wherein the at least onerespiratory motion signal is generated by combining the activityposition versus time curves of only the selected sub-set of regions ofthe array of regions after frequency-selective filtering of the activityposition versus time curves to select content in the respiratoryfrequency band.
 3. The emission imaging data processing device of claim2 wherein: the selection of the sub-set of the regions of the array ofregions is performed for initial successive time intervals; and afterthe selection of the sub-set of regions of the array of regions, thecomputing of the activity position versus time curve and the generatingof the at least one respiratory motion signal is performed using onlythe selected sub-set of regions of the array of regions for subsequentsuccessive time intervals that follow the initial successive timeintervals.
 4. The emission imaging data processing device of claim 2wherein the respiratory motion signal generation method furtherincludes: outputting a zero value for the respiratory motion signal ifthe selected sub-set of regions of the array of regions includes lessthan a threshold number of regions satisfying the region selectioncriterion.
 5. The emission imaging data processing device of claim 1wherein the computing of the activity position versus time curve and thegenerating of the at least one respiratory motion signal are performedconcurrently with the operating of the PET or SPECT imaging device toacquire emission data of the imaging subject in the imaging FOV, and therespiratory motion signal generation method further includes: operatinga display to display the respiratory motion signal as a function of timeduring the operation of the PET or SPECT imaging device.
 6. The emissionimaging data processing device of claim 1 wherein the respiratory motionsignal generation method further includes: defining the array of regionsin the imaging FOV without reference to anatomy of the imaging subject.7. The emission imaging data processing device of claim 1 wherein therespiratory motion signal generation method further includes: definingthe array of regions in the imaging FOV with overlapping of the regions.8. (canceled)
 9. (canceled)
 10. (canceled)
 11. The emission imaging dataprocessing device of claim 1 wherein: the operating comprises operatinga time-of-flight (TOF) PET imaging device to acquire emission datacomprising lines of response (LORs) with TOF localization along theLORs; and the generating of the activity map of the region forsuccessive time intervals from the emission data comprisesback-positioning each LOR to its maximum TOF likelihood position alongthe LOR.
 12. The emission imaging data processing device of claim 1wherein the generating of the at least one respiratory motion signalincludes: frequency-selective filtering the activity position versustime curves to select content in the respiratory frequency band;computing a correlation of each filtered activity position versus timecurve with a reference respiratory motion signal; and adding orsubtracting each filtered activity position versus time curve to therespiratory motion signal wherein the adding or subtracting is chosenbased on a sign of the correlation.
 13. The emission imaging dataprocessing device of claim 12 wherein the generating of the at least onerespiratory motion signal includes: frequency-selective filtering theactivity position versus time curves to select content in therespiratory frequency band; computing a correlation of each filteredactivity position versus time curve with a reference respiratory motionsignal; dividing the imaging FOV into two or more portions based on thecomputed correlations; and for each portion of the imaging FOV,combining the filtered activity position versus time curves for regionsin that portion of the imaging FOV to generate a respiratory motionsignal for that portion of the imaging FOV.
 14. A motion signalgeneration method operating on emission data of an imaging subject in animaging field of view (FOV) acquired by a positron emission tomography(PET) or single photon emission computed tomography (SPECT) imagingdevice, the motion signal generation method comprising: defining anarray of regions in the imaging FOV without reference to anatomy of theimaging subject; for each region of the array of regions defined in theimaging FOV, computing an activity position versus time curve from theemission data acquired by the PET or SPECT imaging device; performingfrequency-selective filtering of the activity position versus timecurves to generate filtered activity position versus time curves; andgenerating at least one motion signal by combining the filtered activityposition versus time curves of at least a sub-set of the regions of thearray of regions to select content in a respiratory frequency band;wherein the computing of the activity position versus time curve fromthe emission data acquired by the PET or SPECT imaging device includes:generating an activity map of the region for successive time intervalsfrom the emission data acquired by the PET or SPECT imaging device; andcomputing values of a statistical activity position descriptor for theactivity maps of the region for the successive time intervals; andwherein the statistical activity position descriptor includes a positionof the centroid of the activity map along a transverse directionparallel with an axial anatomical axis (z) of the imaging subject,and/or a minimum distance of the centroid of the activity map from anaxial anatomical axis of the imaging subject.
 15. The motion signalgeneration method of claim 14 further comprising: selecting a sub-set ofthe regions of the array of regions for which the activity positionversus time curve includes content in a frequency band satisfying aregion selection criterion; wherein the at least one motion signal isgenerated by combining the filtered activity position versus time curvesof only the selected sub-set of regions of the array of regions.
 16. Themotion signal generation method of claim 15 wherein the array of regionsin the imaging FOV is defined with overlapping of the regions. 17.(canceled)
 18. The motion signal generation method of claim 14 whereinthe emission data of the imaging subject in the imaging FOV is acquiredby a PET imaging device with TOF localization, and the generating of theactivity map of the region for successive time intervals from theemission data comprises back-positioning each line of response (LOR) ofthe emission data to its maximum TOF likelihood position along the LOR.19. The motion signal generation method of claim 14 wherein thegenerating of the at least one motion signal includes: computing acorrelation of each filtered activity position versus time curve with areference motion signal; and adding or subtracting each filteredposition versus time curve to the respiratory motion signal wherein theadding or subtracting is chosen based on a sign of the correlation. 20.The motion signal generation method of claim 14 wherein: performingfrequency-selective filtering of the activity position versus timecurves to generate filtered activity position versus time curvescomprises bandpass filtering the activity position versus time curvesusing a bandpass filter having a respiratory frequency passband; and thegenerating comprises generating the at least one motion signal as arespiratory motion signal by combining the bandpass filtered activityposition versus time curves.
 21. An emission imaging data processingdevice comprising: a positron emission tomography (PET) or single photonemission computed tomography (SPECT) imaging device; an electronicprocessor; and a non-transitory storage medium storing instructionsreadable and executable by the electronic processor to perform arespiratory motion signal generation method including: operating the PETor SPECT imaging device to acquire emission data of an imaging subjectin an imaging field of view (FOV); computing activity maps from theemission data for successive time intervals in a region defined in theimaging FOV; computing a transaxial activity position versus time curvefrom the activity maps, the transaxial activity position comprising aminimum distance of the centroid of the activity map from an axialanatomical axis (z) of the imaging subject; and generating a respiratorymotion signal based on at least the transaxial activity position versustime curve.
 22. The emission imaging data processing device of claim 21,wherein the respiratory motion signal generation method furtherincludes: computing a transverse activity position versus time curvefrom the activity maps, the transverse activity position comprising aposition of the centroid of the activity map along a transversedirection parallel with an axial anatomical axis (z) of the imagingsubject; wherein the respiratory motion signal is generated furtherbased on the transverse activity position versus time curve.